1. Field of the Invention
The present invention relates generally to multi-slice computed tomography (CT) imaging systems, and more particularly, to an apparatus and methods of reconstructing an image of an object for an imaging system.
2. Description of the Prior Art
A computed tomography (CT) imaging system typically includes an x-ray source that projects a fan-shaped x-ray beam through an object being imaged, such as a patient, to an array of radiation detectors. The beam is collimated to lie within an X-Y plane, generally referred to as an xe2x80x9cimaging planexe2x80x9d. Intensity of radiation from the beam received at the detector array is dependent upon attenuation of the x-ray beam by the object. Attenuation measurements from each detector are acquired separately to produce a transmission profile.
The x-ray source and the detector array are rotated within a gantry and around the object to be imaged so that a projection angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements, i.e., integral projection data, from the detector array at one gantry angle is referred to as a xe2x80x9cviewxe2x80x9d. A xe2x80x9cscanxe2x80x9d of the object comprises a set of views made at different projection angles.
In an axial scan, the projection data is processed to construct an image that corresponds to a two-dimensional slice taken through the object. For discrete slices, iterative reconstruction of a full field of view may be performed in order to increase image quality. Multiple iterations are performed to approximately match a resulting reconstructed image to the acquired projection data.
Conventional methods for tomographic image reconstruction in single planes from axial mode data may be found in Avinash C. Kak and Malcolm Slaney, xe2x80x9cPrinciples of Computerized Tomographic Imaging,xe2x80x9d Classics in Applied Mathematics, 33, SIAM, 2001, ISBN:089871494X, the entire contents and disclosure of which is hereby incorporated by reference, having been applied especially to X-ray CT since the 1970""s. One of the earliest iterative methods for reconstruction, algebraic reconstruction technique (ART), is also discussed in Avinash C. Kak and Malcolm Slaney, xe2x80x9cPrinciples of Computerized Tomographic Imaging,xe2x80x9d Classics in Applied Mathematics, 33, SIAM, 2001, ISBN:089871494X, the entire contents and disclosure of which is hereby incorporated by reference. References such as A. Delaney and Y. Bresler, xe2x80x9cMultiresolution Tomographic Reconstruction Using Wavelets,xe2x80x9d IEEE Transactions on Image Processing, vol. 4 no. 6, pp. 799-813, June 1995, and B. Sahiner and A. Yagle, xe2x80x9cRegion-of-Interest Tomography Using Exponential Radial Sampling,xe2x80x9d IEEE Transactions on Image Processing, vol. 4 no. 8, pp. 1120-1127, August 1995, the entire contents and disclosures of which are hereby incorporated by reference, use non-iterative reconstruction methods based on alternative signal representations. In references A. Dempster, N. Laird and D. Rubin, xe2x80x9cMaximum Likelihood from Incomplete Data via the EM Algorithm,xe2x80x9d Journal of the Royal Statistical Society B, vol. 1 no. 39, pp. 1-38, 1977, L. Shepp and Y. Vardi, xe2x80x9cMaximum Likelihood Reconstruction for Emission Tomography,xe2x80x9d IEEE Transactions on Medical Imaging, vol. MI-1, no. 2, pp. 113-122, October 1982, and K. Lange and R. Carson, xe2x80x9cEM Reconstruction Algorithms for Emission and Transmission Tomography,xe2x80x9d Journal of Computer Assisted Tomography, vol. 8 no. 2, pp. 306-316, April 1984, the entire contents and disclosures of which are hereby incorporated by reference, the xe2x80x9cexpectation-maximizationxe2x80x9d (EM) technique appears, in the general form, applied to emission tomography, and studied for both emission and transmission (such as X-ray CT). xe2x80x9cOrdered subsetsxe2x80x9d methods for EM are presented in Hudson and Larkin, xe2x80x9cAccelerated Image Reconstruction Using Ordered Subsets of Projection Data,xe2x80x9d IEEE Transactions on Medical Imaging, vol. 13 no. 4, pp. 601-609, December 1994, the entire contents and disclosure of which is hereby incorporated by reference. The Bayesian methods of T. Hebert and R. Leahy, xe2x80x9cA Generalized EM Algorithm for 3-D Bayesian Reconstruction from Poisson data Using Gibbs Priors,xe2x80x9d IEEE Transactions on Medical Imaging, vol. 8 no. 2, pp. 194-202, June 1989, the entirc contents and disclosure of which is hereby incorporated by reference, are an example of xe2x80x9cmaximum a posteriorixe2x80x9d (MAP) techniques, and K. Sauer and C. A. Bouman, xe2x80x9cA Local Update Strategy for Iterative Reconstruction from Projections,xe2x80x9d IEEE Transactions on Signal Processing, vol. 41, no. 2, pp. 534-548, February 1993, and C. A. Bouman and K. Sauer, xe2x80x9cA Unified Approach to Statistical Tomography Using Coordinate Descent Optimization,xe2x80x9d EEE Transactions on Image Processing, vol. 5, no. 3, pp. 480-492, March 1996, the entire contents and disclosures of which are hereby incorporated by reference, include MAP techniques with pixel updates. xe2x80x9cSegmentationxe2x80x9d of images is an imaging process, examples of which are found in H. Derin and H. Elliot, xe2x80x9cModeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields,xe2x80x9d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, pp. 39-55, January 1987, and C. Bouman and B. Liu, xe2x80x9cMultiple Resolution Segmentation of Textured Images,xe2x80x9d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-13, no. 2, pp. 99-113, February 1991, the entire contents and disclosures of which are hereby incorporated by reference, and other references cited therein.
To reduce the total scan time required for multiple slices, a xe2x80x9chelicalxe2x80x9d scan may be performed. Helical scan techniques allow for large volumes to be scanned at a quicker rate using a single x-ray source. To perform a xe2x80x9chelicalxe2x80x9d scan, the patient is moved along a z-axis synchronously with the rotation of the gantry, while data for a prescribed number of slices are acquired. Such a system generates a single helix from a fan beam or cone beam helical scan. The helix mapped out by the fan beam or cone beam yields projection data from which images in each prescribed slice may be reconstructed. In addition to reducing scan time, helical scanning provides other advantages such as better use of injected contrast, improved image reconstruction at arbitrary locations, and better three-dimensional images.
In order to reconstruct the image, typically, a filtered backprojection (FBP) reconstruction approach is utilized. In FBP the projection data is filtered before being backprojected onto an image matrix. The filtering mathematically reverses image blurring, restoring the image to an accurate representation of the scanned object. Although FBP provides relatively quick image reconstruction, many approximations occur due to the imaging system""s accounting for geometries and defects in a single iteration, resulting in an image containing blurring and artifacts.
In CT imaging a targeted reconstruction approach is a popular technique for improvement of image quality and spatial resolution. The targeted reconstruction technique involves using a higher resolution for a reconstruction field of view (RFOV) by reconstructing only the targeted area rather than the entire FOV. Since the image matrix size is typically limited, sampling density within the RFOV can be significantly improved by limiting size of the RFOV. The targeted reconstruction technique ensures that spatial resolution of the reconstructed image is limited by scanning hardware capabilities and not by matrix size of the image. 1For example, when the RFOV is 50 cm by 50 cm each image pixel is approximately 1 mm by 1 mm in size, versus 0.2 mm by 0.2 mm in size when the RFOV is 10 cm by 10 cm. For the RFOV of 50 cm by 50 cm, based on Nyquist sampling theory, the maximum supported spatial resolution is 5 line pairs (LP) per centimeter and for the RFOV of 10 cm by 10 cm the maximum supported spatial resolution is 25 LP/cm.
For filtered backprojection (FBP), targeted reconstruction is nearly identical to full FOV reconstruction. Projection data is weighted and filtered in a fashion similar to full FOV reconstruction. During backprojection, only the scanned area corresponding to the RFOV is utilized from the projection data. Reconstruction is not performed on remaining scanned area outside the RFOV.
Unfortunately, targeted reconstruction is not applicable to iterative reconstruction for either discrete slices or for continuous multi-slice scanning. In iterative reconstruction, forward projection samples based on the reconstructed image need to be compared with the acquired or measured projections. The difference between the forward projection samples and the measured projections are used as a basis for updating the reconstructed image.
When the reconstructed image represents a small portion of the scanned object, the forward projections are an under-estimation of measured projections. Consequently, inherent bias is designed into the iterative reconstruction and the imaging system is not able to match the projection data in a manner which reconstructs an image that accurately represents the actual scanned object. Thus, information of the object outside a region-of-interest is lacking in current systems, which negatively effects estimated projections.
One reason that filtered backprojection is commonly used over iterative reconstruction is its speed. With ever increasing computational speed of computers and a desire for improved image quality is a need for and feasibility of iterative reconstruction.
It would therefore be desirable to provide a method of iterative image reconstruction that minimizes noise and artifacts in both single slice and multi-slice CT imaging systems, and at the same time maintains or increases image quality for a targeted region-of-interest.
The present invention provides an apparatus and methods for reconstructing an image of an object for an imaging system. An imaging system is provided, including a source generating an x-ray beam and a detector array receiving the x-ray beam and generating projection data corresponding to at least a scanned portion of an object. An image reconstructor is electrically coupled to the detector array and reconstructs an image of the scanned portion of the object for a full field-of-view in response to the projection data using a dual iterative reconstruction technique. The dual iterative reconstruction technique includes reconstructing the full field-of-view using a first resolution and reconstructing a region-of-interest within the full FOV using a second resolution. A method is also provided for performing the same.
One of several advantages of the present invention is that it provides a reconstructed image for a full FOV having standard or lower resolution and, via iterative reconstruction, higher resolution for a region-of-interest within the full FOV. Thus, computation is reduced for areas outside the region-of-interest.
Another advantage of the present invention is that it accounts for areas outside the region-of-interest to increase accuracy of projection estimation and decrease time of convergence during iterative reconstruction.
Furthermore, the present invention provides a reconstructed image of improved quality, especially in a region-of-interest, which for diagnostic examination purposes is of higher importance.
Moreover, the present invention provides multiple dual iterative reconstruction techniques and is thus versatile in application, since one dual iterative reconstruction technique may be better for one application over another.
According to a first broad aspect of the present invention, there is provided a imaging system comprising: a source generating a x-ray beam; a detector array receiving the x-ray beam and generating projection data corresponding to at least a scanned portion of an object based on the received x-ray beam; and an image reconstructor electrically coupled to the detector array for reconstructing an image of the scanned portion of the object for a full field-of-view in response to the projection data using a dual iterative reconstruction technique comprising: reconstructing the full field-of-view using a first resolution; and reconstructing a region-of-interest within the full field-of-view using a second resolution.
According to second broad aspect of the invention, there is provided a method of reconstructing an image of an object for an imaging system comprising: providing projection data corresponding to at least a scanned portion of an object; and reconstructing an image of the scanned portion of the object for a full field-of-view based on the projection data using a dual iterative reconstruction technique comprising: reconstructing the full field-of-view using a first resolution; and reconstructing a region-of-interest within the full field-of-view using a second resolution.